Data Set Of Light Curves Research Articles

LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars

With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.

Apparent non-variable stars from the Kepler mission

The analysis of non-variable stars is generally neglected in the literature. However, such objects are needed for many calibration processes and for testing pulsational models. The photometric time series of the Kepler satellite mission still stand as the most accurate data available today and are excellently suited to the search for non-variable stars. We analysed all long-cadence light curves for stars not reported as a variable so far from the Kepler satellite mission. Using the known characteristics and flaws of these data sets, we defined three different frequency ranges where we searched for non-variability. We used the Lomb-Scargle periodogram and the false-alarm probability (FAP) to analyse the cleaned data sets of light curves. We then used $ FAP -2$ to define a star as 'non-variable' in the ranges below 0.1\,c/d, 0.1 to 2.0\,c/d, and 2.0 to 25.0\,c/d, respectively. Furthermore, we also calculated the standard deviation of the mean light curve to obtain another parameter. In total, we found stars that fulfil the set criteria. These objects are mostly cooler than the K populating the whole main sequence (MS) to the red giant branch (RGB).

SPECTROSCOPIC & PHOTOMETRIC STUDY OF THE ALGOL-TYPE BINARY V1241 TAURI

New radial velocity (RV) data obtained at the Dominion Astrophysical Observatory (DAO) in Victoria, British Columbia along with light curve (LC) data for the Algol-type binary V1241 Tau have been simultaneously analysed with the 2003 version of the Wilson-Devinney code (WD2003). There were two distinct LC datasets: one was from the Transiting Exoplanet Survey Satellite (TESS) and the others (BVIc) from the (land-based) Desert Blooms Observatory (DBO). The TESS data were considered to have the least photometric uncertainty; consequently, we derived estimates for M1 (1.91(8) M☉), M2 (1.04(4) M☉), R1 (1.86(1) R☉), R2 (1.73(1) R☉), qWD (0.54(3)), L1 (10.7(8) L☉), and L2 (1.7(2) L☉) following simultaneous analysis (RV+LC) with the WD2003 code. Evolutionary modeling revealed that the primary star is somewhat evolved past the zero age main sequence (ZAMS) while the secondary is much evolved past the terminal age main sequence (TAMS).

Fast Fourier Transformation Based Evaluation of Microlensing Magnification with Extended Source

The extended source effect on microlensing magnification is nonnegligible and must be taken into account for an analysis of microlensing. However, the evaluation of the extended source magnification is numerically expensive because it includes the two-dimensional integral over the source profile. Various studies have developed methods to reduce this integral down to the one-dimensional-integral- or integral-free form, which adopt some approximations or depend on the exact form of the source profile, e.g., a disk or linear/quadratic limb-darkening profile. In this paper, we develop a new method to evaluate the extended source magnification based on fast Fourier transformation (FFT), which does not adopt any approximations and is applicable to any source profiles. Our implementation of the FFT based method enables the fast evaluation of the extended source magnification as fast as ∼1 ms (CPU time on a laptop) and guarantees an accuracy better than 0.3%. The FFT based method can be used for the template fitting to a huge data set of light curves from the existing and upcoming surveys.

44 Boo – A unified integrated light curve analysis

Radial velocity (RV) data from Lu et al. (2001) were combined with light curve (LC) data from the Transiting Exoplanet Survey Satellite (TESS) in an integrated Wilson-Devinney analysis to yield fundamental parameters. Obtained were masses M 1 = 0.526 (5), M ʘ, and M 2 = 1.063 (7) M ʘ, stellar radii R1 = 0.65 (2) Rʘ, and R2 = 0.90 (1) Rʘ, and luminosities L1 = 0.40 (4) Lʘ and L2 = 0.62 (5) Lʘ. These are in reasonable agreement with the values from Hill et al. (1989). Additional analyses were carried out also using the same RV data but substituting, in turn, the LC datasets of Binnendijk (1955), Duerbeck (1978), Robb and Milone (1982) and Genet et al. (1982). While the four Earthbound LC datasets were of much lower precision than the satellite data, they still yielded reasonable agreement in the final parameters. Additionally, they yielded some insight as to the behaviour of the dark spot (on star 1) over some 75 years. The B and V light curve data of Duerbeck (1978) suggested that the unresolved companion is hot. An updated period study originally presented in Nelson et al. (2014) but now with a Light Time Effect (LiTE) fit is presented. Fifteen new times of minimum from the TESS data were added, requiring only a modest refinement of the LiTE parameters. The intrinsic rate of period change (that is, with the LiTE component removed) is dP/dt = 1.80 (20) x 10−7 seconds/year. If dP/dt can be attributed to conservative mass exchange, the rate would be dM1/dt = -2.33 (26) x 10−7 Mʘ/year. When the fundamental parameters for each star were added to the Log L vs Log T plot for EW-type binaries from Yakut and Eggleton (2005), the results suggested that star 1 of the W-type binary is under-luminous, while the cooler but larger secondary is somewhat evolved. Both are in a marginal contact, typical of W-type eclipsing binaries.

Clustering of eclipsing binary light curves through functional principal component analysis

In this paper, we revisit the problem of clustering 1318 new variable stars found in the Milky way. Our recent work distinguishes these stars based on their light curves which are univariate series of brightness from the stars observed at discrete time points. This work proposes a new approach to look at these discrete series as continuous curves over time by transforming them into functional data. Then, functional principal component analysis is performed using these functional light curves. Clustering based on the significant functional principal components reveals two distinct groups of eclipsing binaries with consistency and superiority compared to our previous results. This method is established as a new powerful light curve-based classifier, where implementation of a simple clustering algorithm is effective enough to uncover the true clusters based merely on the first few relevant functional principal components. Simultaneously we discard the noise from the data study involving the higher order functional principal components. Thus the suggested method is very useful for clustering big light curve data sets which is also verified by our simulation study.

A transfer learning approach to space debris classification using observational light curve data

This paper presents a data driven approach to space object characterisation through the application of machine learning techniques to observational light curve data. One-dimensional convolutional neural networks are shown to be effective at classifying the shape of objects from both simulated and real light curve data. To the best of the authors’ knowledge this is the first generalised attempt to classify the shape of space objects using real observational light curve data.It is also demonstrated that transfer learning is successful in improving the overall classification accuracy on real light curve datasets. The authors develop a simulated light curve dataset using a high fidelity three-dimensional ray-tracing software. The simulator takes in a textured geometric model of a Resident Space Object as well as its ephemeris and uses ray-tracing software to generate photo-realistic images of the object that are then processed to extract the light curve. Models that are pre-trained on the simulated dataset and then fine-tuned on the real datasets are shown to outperform models purely trained on the real datasets. This result indicates that transfer learning will allow organisations to effectively utilise deep learning techniques without the requirement to build up large real light curve datasets for training.

Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey

Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized when light curves and radial velocity curves are available. The manual examination of these datasets is a task that requires big quantities of time and effort, and therefore is prone to errors. As a result, the application of machine learning methods has become more common on exoplanet discovery and categorization research. This survey presents an analysis on different exoplanet transit discovery algorithms based on machine learning, some of which even found new exoplanets. The analysis of these algorithms is divided into four steps, namely light curve preprocessing, possible exoplanet signal detection, and identification of the detected signal to decide whether it belongs to an exoplanet or not. We propose a model to create synthetic datasets of light curves, and we compare the performance of several machine learning models used to identify transit exoplanets, with inputs preprocessed with and without using the Discrete Wavelet Transform (DWT). Our experimental results allow us to conclude that multiresolution analysis in the time-frequency domain can improve exoplanet signal identification, because of the characteristics of light curves and transiting exoplanet signals.

Initial Evaluation of SNEMO2 and SNEMO7 Standardization Derived from Current Light Curves of Type Ia Supernovae

Abstract To determine if the SuperNova Empirical Model (SNEMO) can improve Type Ia supernova (SN Ia) standardization of several currently available photometric data sets, we perform an initial test, comparing results with the much-used SALT2 approach. We fit the SNEMO light-curve parameters and pass them to the Bayesian hierarchical model UNITY1.2 to estimate the Tripp-like standardization coefficients, including a host-mass term as a proxy for redshift-dependent astrophysical systematics. We find that, among the existing large data sets, only the Carnegie Supernova Project data set consistently provides the signal-to-noise and time sampling necessary to constrain the additional five parameters that SNEMO7 incorporates beyond SALT2. This is an important consideration for future SN Ia surveys like LSST and WFIRST. Although the SNEMO7 parameters are poorly constrained by most of the other available data sets of light curves, we find that the SNEMO2 parameters are just as well constrained as the SALT2 parameters. In addition, SNEMO2 and SALT2 have comparable unexplained intrinsic scatter when fitting the same data. When looking at the total scatter, SNEMO7 reduces the Hubble–Lemaître diagram rms from 0.148 to 0.141 mag. It is not then the SNEMO methodology, but the interplay of data quality and the increased number of degrees of freedom that is behind these reduced constraints. With this in mind, we recommend further investigation into the data required to use SNEMO7 and the possibility of fitting the poorer photometry data with intermediate SNEMO-like models with three to six components.

Identifying transient and variable sources in radio images

Abstract With the arrival of a number of wide-field snapshot image-plane radio transient surveys, there will be a huge influx of images in the coming years making it impossible to manually analyse the datasets. Automated pipelines to process the information stored in the images are being developed, such as the LOFAR Transients Pipeline, outputting light curves and various transient parameters. These pipelines have a number of tuneable parameters that require training to meet the survey requirements. This paper utilises both observed and simulated datasets to demonstrate different machine learning strategies that can be used to train these parameters. We use a simple anomaly detection algorithm and a penalised logistic regression algorithm. The datasets used are from LOFAR observations and we process the data using the LOFAR Transients Pipeline; however the strategies developed are applicable to any light curve datasets at different frequencies and can be adapted to different automated pipelines. These machine learning strategies are publicly available as Python tools that can be downloaded and adapted to different datasets ( https://github.com/AntoniaR/TraP_ML_tools ).

Optical observations of NEA 3200 Phaethon (1983 TB) during the 2017 apparition

Context. The near-Earth asteroid 3200 Phaethon (1983 TB) is an attractive object not only from a scientific viewpoint but also because of JAXA’s DESTINY+ target. The rotational lightcurve and spin properties were investigated based on the data obtained in the ground-based observation campaign of Phaethon. Aims. We aim to refine the lightcurves and shape model of Phaethon using all available lightcurve datasets obtained via optical observation, as well as our time-series observation data from the 2017 apparition. Methods. Using eight 12-m telescopes and an optical imager, we acquired the optical lightcurves and derived the spin parameters of Phaethon. We applied the lightcurve inversion method and SAGE algorithm to deduce the convex and non-convex shape model and pole orientations. Results. We analysed the optical lightcurve of Phaethon and derived a synodic and a sidereal rotational periods of 3.6039 h, with an axis ratio of a∕b = 1.07. The ecliptic longitude (λp) and latitude (βp) of the pole orientation were determined as (308°, −52°) and (322°, −40°) via two independent methods. A non-convex model from the SAGE method, which exhibits a concavity feature, is also presented.

Transfer Learning in Astronomy: A New Machine-Learning Paradigm

The widespread dissemination of machine learning tools in science, particularly in astronomy, has revealed the limitation of working with simple single-task scenarios in which any task in need of a predictive model is looked in isolation, and ignores the existence of other similar tasks. In contrast, a new generation of techniques is emerging where predictive models can take advantage of previous experience to leverage information from similar tasks. The new emerging area is referred to as transfer learning. In this paper, I briefly describe the motivation behind the use of transfer learning techniques, and explain how such techniques can be used to solve popular problems in astronomy. As an example, a prevalent problem in astronomy is to estimate the class of an object (e.g., Supernova Ia) using a generation of photometric light-curve datasets where data abounds, but class labels are scarce; such analysis can benefit from spectroscopic data where class labels are known with high confidence, but the data sample is small. Transfer learning provides a robust and practical solution to leverage information from one domain to improve the accuracy of a model built on a different domain. In the example above, transfer learning would look to overcome the difficulty in the compatibility of models between spectroscopic data and photometric data, since data properties such as size, class priors, and underlying distributions, are all expected to be significantly different.

A possible close supermassive black-hole binary in a quasar with optical periodicity.

Quasars have long been known to be variable sources at all wavelengths. Their optical variability is stochastic and can be due to a variety of physical mechanisms; it is also well-described statistically in terms of a damped random walk model. The recent availability of large collections of astronomical time series of flux measurements (light curves) offers new data sets for a systematic exploration of quasar variability. Here we report the detection of a strong, smooth periodic signal in the optical variability of the quasar PG 1302-102 with a mean observed period of 1,884±88 days. It was identified in a search for periodic variability in a data set of light curves for 247,000 known, spectroscopically confirmed quasars with a temporal baseline of about 9years. Although the interpretation of this phenomenon is still uncertain, the most plausible mechanisms involve a binary system of two supermassive black holes with a subparsec separation. Such systems are an expected consequence of galaxy mergers and can provide important constraints on models of galaxy formation and evolution.

TRANSMISSION SPECTRA OF THREE-DIMENSIONAL HOT JUPITER MODEL ATMOSPHERES

We compute models of the transmission spectra of planets HD 209458b, HD 189733b, and generic hot Jupiters. We examine the effects of temperature, surface gravity, and metallicity for the generic planets as a guide to understanding transmission spectra in general. We find that carbon dioxide absorption at 4.4 and 15 microns is prominent at high metallicity, and is a clear metallicity indicator. For HD 209458b and HD 189733b, we compute spectra for both one-dimensional and three-dimensional model atmospheres and examine the differences between them. The differences are usually small, but can be large if atmospheric temperatures are near important chemical abundance boundaries. The calculations for the 3D atmospheres, and their comparison with data, serve as constraints on these dynamical models that complement the secondary eclipse and light curve data sets. For HD 209458b, even if TiO and VO gases are abundant on the day side, their abundances can be considerably reduced on the cooler planetary limb. However, given the predicted limb temperatures and TiO abundances, the model's optical opacity is too high. For HD 189733b we find a good match with some infrared data sets and constrain the altitude of a postulated haze layer. For this planet, substantial differences can exist between the transmission spectra of the leading and trailing hemispheres, which is an excellent probe of carbon chemistry. In thermochemical equilibrium, the cooler leading hemisphere is methane-dominated, and the hotter trailing hemisphere is CO-dominated, but these differences may be eliminated by non-equilibrium chemistry due to vertical mixing. It may be possible to constrain the carbon chemistry of this planet, and its spatial variation, with JWST.

Detection of the YORP effect in asteroid (1620) Geographos

Aims. The rotation state of small asteroids is affected by the Yarkovsky-O’Keefe-Radzievskii-Paddack (YORP) torque. The directly observable consequence of the YORP effect is the secular change of the asteroid’s rotation period. We carried out new photometric observations of asteroid (1620) Geographos in 2008 to extend the time line that, if long enough, would enable us to see possible deviations from a constant period rotation. Methods. We used the lightcurve inversion method to model the shape and spin state of Geographos. We assumed that the rotation rate evolves in time as ω(t) = ω0 + υt, where both the constant term of the rotation rate ω0 and the linear term υ are parameters to be optimized. In total, we used 94 lightcurves observed in 1969−2008. Results. We show that for υ = 0, a constant-period model, the whole dataset of lightcurves cannot be satisfactorily fitted. However, when relaxing υ in the optimization process we obtain an excellent agreement between the model and observations. The best-fit value υ = (1.15 ± 0.15) × 10 −8 rad d −2 implies that Geographos’ rotation rate accelerates by � 2. 7m s yr −1 . This is in agreement with the theoretically predicted value 1.4 × 10 −8 rad d −2 obtained from numerical integration of YORP torques acting on our convex shape model. Geographos is only the third asteroid (after (1862) Apollo and (54509) YORP) for which the YORP effect has been detected. It is also the largest object for which effects of thermal torques were revealed.

Solutions for 10,000 Eclipsing Binaries in the Bulge Fields of OGLE II Using DEBiL

We have developed a fully automated pipeline for systematically identifying and analyzing eclipsing binaries within large data sets of light curves. The pipeline is made up of multiple tiers that subject the light curves to increasing levels of scrutiny. After each tier, light curves that did not conform to a given criteria were filtered out of the pipeline, reducing the load on the following, more computationally intensive tiers. As a central component of the pipeline, we created the fully automated Detached Eclipsing Binary Light curve fitter (DEBiL), which rapidly fits large numbers of light curves to a simple model. Using the results of DEBiL, light curves of interest can be flagged for follow-up analysis. As a test case, we analyzed the 218,699 light curves within the bulge fields of the OGLE II survey and produced 10,862 model fits. We point out a small number of extreme examples, as well as unexpected structure found in several of the population distributions. We expect this approach to become increasingly important as light-curve data sets continue growing in both size and number.

Spin vectors in the Koronis family: comprehensive results from two independent analyses of 213 rotation lightcurves

Observations of Koronis asteroid family members (158) Koronis, (277) Elvira, (311) Claudia, (321) Florentina, and (720) Bohlinia made during the period 1998–2001 yielded 61 new individual rotation lightcurves to augment previous surveys (R.P. Binzel, 1987, Icarus 72, 135–208; S.M. Slivan, R.P. Binzel, 1996, Icarus 124, 452–470) and allow determination of the senses of rotation and spin vector orientations for these objects. Spin vector reductions were performed on these five objects and also on family members (167) Urda, (208) Lacrimosa, (534) Nassovia, and (1223) Neckar using both a combination of amplitude–magnitude and epoch methods and a convex inversion method. A total of 213 individual lightcurves were analyzed to determine sidereal rotation periods, pole solutions and obliquities, associated photometric parameters, and model shapes for each object. We checked our methods and results using the (243) Ida Master Dataset of lightcurves (R. P. Binzel et al., 1993, Icarus 105, 310–325) and found that the true pole determined from the Galileo fly by of this irregularly shaped member of the Koronis family falls just at the edge of the estimated uncertainty of our own solution. Our findings for the spin vector distribution of 10 members within the Koronis family represent the first systematic study of spin states within a well-established Hirayama family, and provide observational constraints for models of the physics of family formation and spin vector evolution in the main belt.